
"""simple docstring"""



from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
 from ..models.auto import AutoTokenizer




class 		a_    :







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							Union[str, Any]  )		->Tuple:






        '''simple docstring'''






        raise NotImplementedError()







    def   _snake_case       (      self      :							Optional[Any]  )		->int:






        '''simple docstring'''






        raise NotImplementedError()







class 		a_    (			_UpperCAmelCase						):







    def __init__(      self      :							Optional[Any]  ,       __UpperCamelCase      :							"AutoTokenizer"  ,       __UpperCamelCase      :							bool = False  ,       **__UpperCamelCase      :							int  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = tokenizer
        _UpperCAmelCase        = skip_prompt
        _UpperCAmelCase        = decode_kwargs

        # variables used in the streaming process
        _UpperCAmelCase        = []
        _UpperCAmelCase        = 0
        _UpperCAmelCase        = True







    def   _snake_case       (      self      :							Optional[Any]  ,       __UpperCamelCase      :							Union[str, Any]  )		->Union[str, Any]:






        '''simple docstring'''






        if len(value.shape  ) > 1 and value.shape[0] > 1:
            raise ValueError("""TextStreamer only supports batch size 1"""  )
        elif len(value.shape  ) > 1:
            _UpperCAmelCase        = value[0]

        if self.skip_prompt and self.next_tokens_are_prompt:
            _UpperCAmelCase        = False
            return

        # Add the new token to the cache and decodes the entire thing.
        self.token_cache.extend(value.tolist()  )
        _UpperCAmelCase        = self.tokenizer.decode(self.token_cache  ,       **self.decode_kwargs  )

        # After the symbol for a new line, we flush the cache.
        if text.endswith("""\n"""  ):
            _UpperCAmelCase        = text[self.print_len :]
            _UpperCAmelCase        = []
            _UpperCAmelCase        = 0
        # If the last token is a CJK character, we print the characters.
        elif len(__UpperCamelCase  ) > 0 and self._is_chinese_char(ord(text[-1]  )  ):
            _UpperCAmelCase        = text[self.print_len :]
            self.print_len += len(__UpperCamelCase  )
        # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
        # which may change with the subsequent token -- there are probably smarter ways to do this!)
        else:
            _UpperCAmelCase        = text[self.print_len : text.rfind(""" """  ) + 1]
            self.print_len += len(__UpperCamelCase  )

        self.on_finalized_text(__UpperCamelCase  )







    def   _snake_case       (      self      :							List[Any]  )		->List[Any]:






        '''simple docstring'''






        if len(self.token_cache  ) > 0:
            _UpperCAmelCase        = self.tokenizer.decode(self.token_cache  ,       **self.decode_kwargs  )
            _UpperCAmelCase        = text[self.print_len :]
            _UpperCAmelCase        = []
            _UpperCAmelCase        = 0
        else:
            _UpperCAmelCase        = """"""

        _UpperCAmelCase        = True
        self.on_finalized_text(__UpperCamelCase  ,       stream_end=__UpperCamelCase  )







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							bool = False  )		->Union[str, Any]:






        '''simple docstring'''






        print(__UpperCamelCase  ,       flush=__UpperCamelCase  ,       end="""""" if not stream_end else None  )







    def   _snake_case       (      self      :							Optional[int]  ,       __UpperCamelCase      :							List[Any]  )		->Dict:






        '''simple docstring'''






        if (
            (cp >= 0x4e00 and cp <= 0x9fff)
            or (cp >= 0x3400 and cp <= 0x4dbf)  #
            or (cp >= 0x20000 and cp <= 0x2a6df)  #
            or (cp >= 0x2a700 and cp <= 0x2b73f)  #
            or (cp >= 0x2b740 and cp <= 0x2b81f)  #
            or (cp >= 0x2b820 and cp <= 0x2ceaf)  #
            or (cp >= 0xf900 and cp <= 0xfaff)
            or (cp >= 0x2f800 and cp <= 0x2fa1f)  #
        ):  #
            return True

        return False







class 		a_    (			_UpperCAmelCase						):







    def __init__(      self      :							Dict  ,       __UpperCamelCase      :							"AutoTokenizer"  ,       __UpperCamelCase      :							bool = False  ,       __UpperCamelCase      :							Optional[float] = None  ,       **__UpperCamelCase      :							Tuple  )		->Optional[Any]:






        '''simple docstring'''






        super().__init__(__UpperCamelCase  ,       __UpperCamelCase  ,       **__UpperCamelCase  )
        _UpperCAmelCase        = Queue()
        _UpperCAmelCase        = None
        _UpperCAmelCase        = timeout







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							bool = False  )		->List[str]:






        '''simple docstring'''






        self.text_queue.put(__UpperCamelCase  ,       timeout=self.timeout  )
        if stream_end:
            self.text_queue.put(self.stop_signal  ,       timeout=self.timeout  )







    def __iter__(      self      :							Optional[Any]  )		->List[str]:






        '''simple docstring'''






        return self







    def   _snake_case       (      self      :							Any  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.text_queue.get(timeout=self.timeout  )
        if value == self.stop_signal:
            raise StopIteration()
        else:
            return value

"""simple docstring"""



import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = OmegaConf.load(_A   )
    if display:
        print(yaml.dump(OmegaConf.to_container(_A   )   )   )
    return config


def 			_UpperCamelCase						(    _A       ,   _A=None       ,   _A=None   )     ->	List[Any]:





    """simple docstring"""

    if conf_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.yaml"""
    _UpperCAmelCase        = load_config(_A       ,   display=_A   )
    _UpperCAmelCase        = VQModel(**config.model.params   )
    if ckpt_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.pt"""
    _UpperCAmelCase        = torch.load(_A       ,   map_location=_A   )
    if ".ckpt" in ckpt_path:
        _UpperCAmelCase        = sd["""state_dict"""]
    model.load_state_dict(_A       ,   strict=_A   )
    model.to(_A   )
    del sd
    return model


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = model.encode(_A   )
    print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""   )
    _UpperCAmelCase        = model.decode(_A   )
    return xrec


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = string.rsplit("""."""       ,   1   )
    if reload:
        _UpperCAmelCase        = importlib.import_module(_A   )
        importlib.reload(_A   )
    return getattr(importlib.import_module(_A       ,   package=_A   )       ,   cls   )


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    if "target" not in config:
        raise KeyError("""Expected key `target` to instantiate."""   )
    return get_obj_from_str(config["""target"""]   )(**config.get("""params"""       ,   {}   )   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=True       ,   _A=True   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = instantiate_from_config(_A   )
    if sd is not None:
        model.load_state_dict(_A   )
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A   )     ->	Optional[Any]:





    """simple docstring"""

    if ckpt:
        _UpperCAmelCase        = torch.load(_A       ,   map_location="""cpu"""   )
        _UpperCAmelCase        = pl_sd["""global_step"""]
        print(F"""loaded model from global step {global_step}."""   )
    else:
        _UpperCAmelCase        = {"""state_dict""": None}
        _UpperCAmelCase        = None
    _UpperCAmelCase        = load_model_from_config(config.model       ,   pl_sd["""state_dict"""]       ,   gpu=_A       ,   eval_mode=_A   )["""model"""]
    return model, global_step